"""simple docstring"""



import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__:List[str]									=			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Any							  =						BartphoTokenizer
 _snake_case       :					Union[str, Any]							  =						False
 _snake_case       :					Any							  =						True


 def 						a__   ( self						):
        super().setUp()

        __a       =    ["▁This", "▁is", "▁a", "▁t", "est"]
        __a       =    dict(zip(lowerCamelCase    ,		range(len(lowerCamelCase						)						)						)						)
        __a       =    {"unk_token": "<unk>"}

        __a       =    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES["monolingual_vocab_file"]						)
        with open(self.monolingual_vocab_file    ,		"w"    ,		encoding="utf-8"						) as fp:
               for token in vocab_tokens:
                      fp.write(F"{token} {vocab_tokens[token]}\n"						)

        __a       =    BartphoTokenizer(lowerCamelCase    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map						)
        tokenizer.save_pretrained(self.tmpdirname						)


 def 						a__   ( self    ,		**lowerCamelCase						):
        kwargs.update(self.special_tokens_map						)
        return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    "This is a là test"
        __a       =    "This is a<unk><unk> test"
        return input_text, output_text






 def 						a__   ( self						):
        __a       =    BartphoTokenizer(lowerCamelCase    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map						)
        __a       =    "This is a là test"
        __a       =    "▁This ▁is ▁a ▁l à ▁t est".split()
        __a       =    tokenizer.tokenize(lowerCamelCase						)
        self.assertListEqual(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    tokens + [tokenizer.unk_token]
        __a       =    [4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase						)    ,		lowerCamelCase						)

"""simple docstring"""



from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE__:str									=			HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str]									=			parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE__:List[Any]									=			AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			{
    """vocab_size""": len(tokenizer),
    """scale_attn_by_inverse_layer_idx""": True,
    """reorder_and_upcast_attn""": True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE__:Optional[int]									=			AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE__:Tuple									=			AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
